





'''simple docstring'''






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
					from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=2		,   a_=32		,   a_=16		,   a_=3		,   a_=True		,   a_=True		,   a_=32		,   a_=4		,   a_=[0, 1, 2, 3]		,   a_=4		,   a_=37		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=0.02		,   a_=3		,   a_=[1, 384, 24, 24]		,   a_=True		,   a_=None		,   ):
			lowerCAmelCase  :     Any       						= parent
			lowerCAmelCase  :     Optional[int]       						= batch_size
			lowerCAmelCase  :     Optional[int]       						= image_size
			lowerCAmelCase  :     Any       						= patch_size
			lowerCAmelCase  :     Optional[Any]       						= num_channels
			lowerCAmelCase  :     Dict       						= is_training
			lowerCAmelCase  :     Optional[int]       						= use_labels
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     str       						= num_hidden_layers
			lowerCAmelCase  :     int       						= backbone_out_indices
			lowerCAmelCase  :     Optional[int]       						= num_attention_heads
			lowerCAmelCase  :     Optional[int]       						= intermediate_size
			lowerCAmelCase  :     List[str]       						= hidden_act
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout_prob
			lowerCAmelCase  :     Dict       						= attention_probs_dropout_prob
			lowerCAmelCase  :     Dict       						= initializer_range
			lowerCAmelCase  :     Optional[int]       						= num_labels
			lowerCAmelCase  :     Optional[Any]       						= backbone_featmap_shape
			lowerCAmelCase  :     List[str]       						= scope
			lowerCAmelCase  :     int       						= is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCAmelCase  :     Any       						= (image_size // patch_size) ** 2
			lowerCAmelCase  :     Optional[Any]       						= num_patches + 1



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

			lowerCAmelCase  :     Union[str, Any]       						= None
			if self.use_labels:
					lowerCAmelCase  :     Dict       						= ids_tensor([self.batch_size, self.image_size, self.image_size]		,   self.num_labels						)

			lowerCAmelCase  :     List[str]       						= self.get_config()

			return config, pixel_values, labels



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= {
			    "global_padding": "same",
			    "layer_type": "bottleneck",
			    "depths": [3, 4, 9],
			    "out_features": ["stage1", "stage2", "stage3"],
			    "embedding_dynamic_padding": True,
			    "hidden_sizes": [96, 192, 384, 768],
			    "num_groups": 2,
			}

			return DPTConfig(
			    image_size=self.image_size		,   patch_size=self.patch_size		,   num_channels=self.num_channels		,   hidden_size=self.hidden_size		,   num_hidden_layers=self.num_hidden_layers		,   backbone_out_indices=self.backbone_out_indices		,   num_attention_heads=self.num_attention_heads		,   intermediate_size=self.intermediate_size		,   hidden_act=self.hidden_act		,   hidden_dropout_prob=self.hidden_dropout_prob		,   attention_probs_dropout_prob=self.attention_probs_dropout_prob		,   is_decoder=a_		,   initializer_range=self.initializer_range		,   is_hybrid=self.is_hybrid		,   backbone_config=a_		,   backbone_featmap_shape=self.backbone_featmap_shape		,   )



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= DPTModel(config=a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     str       						= model(a_						)
			self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, self.seq_length, self.hidden_size)						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Tuple       						= self.num_labels
			lowerCAmelCase  :     Tuple       						= DPTForDepthEstimation(a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     Union[str, Any]       						= model(a_						)
			self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     str       						= self.num_labels
			lowerCAmelCase  :     int       						= DPTForSemanticSegmentation(a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     int       						= model(a_		,   labels=a_						)
			self.parent.assertEqual(
			    result.logits.shape		,   (self.batch_size, self.num_labels, self.image_size, self.image_size)						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self.prepare_config_and_inputs()
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     int       						= config_and_inputs
			lowerCAmelCase  :     Union[str, Any]       						= {"pixel_values": pixel_values}
			return config, inputs_dict






@require_torch
class 		lowerCamelCase	(   _A   , _A   , unittest.TestCase							):
	snake_case_   = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	snake_case_   = (
	    {
	        "depth-estimation": DPTForDepthEstimation,
	        "feature-extraction": DPTModel,
	        "image-segmentation": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case_   = False
	snake_case_   = False
	snake_case_   = False



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= DPTModelTester(self						)
			lowerCAmelCase  :     List[str]       						= ConfigTester(self		,   config_class=a_		,   has_text_modality=a_		,   hidden_size=37						)



	def 	_lowerCamelCase     ( self						):
			self.config_tester.run_common_tests()



	@unittest.skip(reason="DPT does not use inputs_embeds"						)
	def 	_lowerCamelCase     ( self						):
			pass



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase  :     List[Any]       						= model_class(a_						)
					self.assertIsInstance(model.get_input_embeddings()		,   (nn.Module)						)
					lowerCAmelCase  :     Optional[Any]       						= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(a_		,   nn.Linear						)						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase  :     Optional[Any]       						= model_class(a_						)
					lowerCAmelCase  :     Union[str, Any]       						= inspect.signature(model.forward						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCAmelCase  :     Any       						= [*signature.parameters.keys()]

					lowerCAmelCase  :     Union[str, Any]       						= ["pixel_values"]
					self.assertListEqual(arg_names[:1]		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*a_						)



	def 	_lowerCamelCase     ( self						):
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

					lowerCAmelCase						,		lowerCAmelCase  :     Any       						= self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase  :     List[str]       						= True

					if model_class in get_values(a_						):
							continue

					lowerCAmelCase  :     Any       						= model_class(a_						)
					model.to(a_						)
					model.train()
					lowerCAmelCase  :     Any       						= self._prepare_for_class(a_		,   a_		,   return_labels=a_						)
					lowerCAmelCase  :     str       						= model(**a_						).loss
					loss.backward()



	def 	_lowerCamelCase     ( self						):
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

					lowerCAmelCase						,		lowerCAmelCase  :     Any       						= self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase  :     Union[str, Any]       						= False
					lowerCAmelCase  :     int       						= True

					if model_class in get_values(a_						) or not model_class.supports_gradient_checkpointing:
							continue
					lowerCAmelCase  :     Optional[int]       						= model_class(a_						)
					model.to(a_						)
					model.gradient_checkpointing_enable()
					model.train()
					lowerCAmelCase  :     List[Any]       						= self._prepare_for_class(a_		,   a_		,   return_labels=a_						)
					lowerCAmelCase  :     Optional[Any]       						= model(**a_						).loss
					loss.backward()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     int       						= self.model_tester.prepare_config_and_inputs_for_common()

			lowerCAmelCase  :     Optional[int]       						= _config_zero_init(a_						)
			for model_class in self.all_model_classes:
					lowerCAmelCase  :     Optional[int]       						= model_class(config=a_						)
					# Skip the check for the backbone
					lowerCAmelCase  :     str       						= []
					for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									lowerCAmelCase  :     Tuple       						= [F'''{name}.{key}''' for key in module.state_dict().keys()]
									break

					for name, param in model.named_parameters():
							if param.requires_grad:
									if name in backbone_params:
											continue
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()		,   [0.0, 1.0]		,   msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''		,   )



	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
	def 	_lowerCamelCase     ( self						):
			pass



	@slow
	def 	_lowerCamelCase     ( self						):
			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
					lowerCAmelCase  :     str       						= DPTModel.from_pretrained(a_						)
					self.assertIsNotNone(a_						)





	def 	_lowerCamelCase     ( self						):
			# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
			lowerCAmelCase						,		lowerCAmelCase  :     int       						= self.model_tester.prepare_config_and_inputs_for_common()
			lowerCAmelCase  :     Optional[Any]       						= "add"
			with self.assertRaises(a_						):
					lowerCAmelCase  :     Dict       						= DPTForDepthEstimation(a_						)






def   __A    (				):
		lowerCAmelCase  :     Union[str, Any]       						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
		return image

@require_torch
@require_vision
@slow
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"						)
			lowerCAmelCase  :     Optional[Any]       						= DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"						).to(a_						)

			lowerCAmelCase  :     Optional[int]       						= prepare_img()
			lowerCAmelCase  :     Any       						= image_processor(images=a_		,   return_tensors="pt"						).to(a_						)

			# forward pass
			with torch.no_grad():
					lowerCAmelCase  :     Any       						= model(**a_						)
					lowerCAmelCase  :     Tuple       						= outputs.predicted_depth

			# verify the predicted depth
			lowerCAmelCase  :     Tuple       						= torch.Size((1, 384, 384)						)
			self.assertEqual(predicted_depth.shape		,   a_						)

			lowerCAmelCase  :     Optional[int]       						= torch.tensor(
			    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]						).to(a_						)

			self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100		,   a_		,   atol=1e-4						)						)










'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



